发表机构
Sungkyunkwan University（成均馆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对前馈3DGS中瞬态物体引发的伪影问题，提出无需训练的干扰物过滤流程，可在多模型和基准上提升新视图质量且保留干净场景原始重建。
AI 中文摘要
前馈3D高斯溅射（3DGS）通过一次网络执行从多张输入图像重建显式高斯表示，使普通拍摄的3D重建愈发便捷。但此类拍摄常包含仅出现在部分视图中的瞬态物体，这些内容会被编码至观测到它的输入对应的逐视图高斯中，即便不再被其他输入观测也会保留在组合表示中，进而在新视图中产生模糊、重复或漂浮的伪影。我们提出一种利用该逐视图预测结构的无需训练的过滤流程：对每个输入，排除其关联的高斯并使用剩余表示渲染同一相机，以揭示与其他输入不一致的内容；通过特征相似性形成候选区域，再基于渲染的验证仅保留那些移除后可降低其他输入视图重建误差的候选。该流程在单个冻结预测上运行，无需重新训练或场景特定优化。在三个重建模型和两个干扰物基准上，它在不同输入视图数量下均能持续提升新视图质量；在干净场景中，对四个模型的评估显示原始重建被大量保留。
英文摘要
Feed-forward 3D Gaussian Splatting reconstructs an explicit Gaussian representation from multiple input images in one network execution, making 3D reconstruction increasingly accessible for casual captures. However, such captures frequently contain transient objects that appear in only a subset of the views. Such content can be encoded into the per-view Gaussians associated with the inputs that observe it and remain in the combined representation despite being observed by no other input. As a result, it may produce blurred, duplicated, or floating artifacts in novel views. We introduce a training-free filtering procedure that exploits this per-view prediction structure. For each input, we exclude its associated Gaussians and render the same camera using the remaining representation, revealing content that is inconsistent with the other inputs. Feature similarity forms candidate regions, and rendering-based verification retains only candidates whose removal reduces reconstruction error in the other input views. The procedure operates on a single frozen prediction without retraining or scene-specific optimization. Across three reconstruction models and two distractor benchmarks, it consistently improves novel-view quality with varying numbers of input views. On clean scenes, evaluations across four models show that the original reconstructions are largely preserved.
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